Deep Learning of Player Trajectory Representations for Team Activity Analysis

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Abstract: Team sports such as hockey and basketball involve complex player interactions. Modeling how players interact presents a great challenge to researchers in the field of sports analysis. The most common source of data available for this type of analysis is player trajectory data, which encode vital information about the motion, action, and intention of players. At an individual level, each player exhibits a characteristic trajectory style that can distinguish him from other players. At a team level, a set of player trajectories forms unique dynamics that differentiate the team from others. We believe both players and teams possess their own particular spatio-temporal patterns hidden in the trajectory data and we propose a generic deep learning model that learns powerful representations from player trajectories. In brief, we use layers of 1D convolutions to learn discriminative feature representations from player tracking data while also resolving the permutation problem inherent in player tracking data. With the learned representations, our model can automatically recognize events, identify players, and classify teams. We show that, on the Sportlogiq hockey dataset, our model with only trajectories as input outperforms a deep neural network that takes videos as input, on the task of event recognition. Our model achieves even better performance when used in combination with videos. We also demonstrate, on a basketball dataset, how our model excels at team classification using only player trajectories. We believe these deep learning trajectory representations have potential for varied applications in understanding and predicting player and team activities in sports analytics.

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